Method, apparatus, and computer program product for identifying hazardous conditions and predicting policy transaction behavior

ABSTRACT

Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for programmatically predicting policy transactions using machine learning.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/556,110, titled “METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCTFOR IDENTIFYING HAZARDOUS CONDITIONS AND PREDICTING POLICY TRANSACTIONBEHAVIOR,” and filed Sep. 8, 2017, the contents of which areincorporated herein by reference in their entirety.

BACKGROUND

External physical plots of land and structures have hazards associatedwith them. Programmatically making decisions to provide policies toinsure such external plots and structures can be difficult in view ofsuch hazards. Through applied effort, ingenuity, and innovation, many ofthese identified problems have been solved by developing solutions thatare included in embodiments of the present disclosure, many examples ofwhich are described in detail herein.

BRIEF SUMMARY

Embodiments of the present disclosure relate to concepts for predictingpolicy transaction behavior. In one embodiment, a computing entity orapparatus is configured to receive, from a remote computing device, aplurality of digital image files associated with the external plot ofphysical land. In embodiments, each digital image file of the pluralityof digital image files is representative of a different field of view ofthe external plot of physical land. In embodiments, the computing entityor apparatus is further configured to extract, from each digital imagefile of the plurality of digital image files, one or more image-basedplot properties associated with the external plot of physical land. Inembodiments, the extracting is performed on a pixel by pixel basis andusing a first trained machine learning model. In embodiments, the firsttrained machine learning model outputs a predicted image-based plotproperty based on one or more pixels of a digital image file and aconfidence measure associated with the predicted image-based plotproperty. In embodiments, the computing entity or apparatus is furtherconfigured to generate, for each image-based plot property and using asecond trained machine learning model, a policy transaction prediction.In embodiments, the policy transaction prediction being aprogrammatically generated likelihood that the image-based plot propertywill lead to a policy transaction occurrence. In embodiments, thecomputing entity or apparatus is further configured to, based on thepolicy transaction predictions, generate an aggregated policytransaction prediction for the external plot of physical land. Inembodiments, the aggregated policy transaction prediction being aprogrammatically generated likelihood that a policy transaction willoccur subsequent an insurance policy issuance for the external plot ofphysical land. In embodiments, the computing entity or apparatus isfurther configured to transmit, to a requesting computing device, theaggregated policy transaction prediction.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the disclosure in general terms, reference willnow be made to the accompanying drawings, which are not necessarilydrawn to scale, and wherein:

FIG. 1 is a system architecture diagram of system configured to practiceembodiments of the present disclosure;

FIG. 2 is an exemplary schematic diagram of a computing entity accordingto one embodiment of the present disclosure;

FIG. 3 is a flow chart of an exemplary process for plot risk selectionand pricing for use with embodiments of the present disclosure;

FIG. 4A illustrates an exemplary process for predicting policytransaction behavior for use with embodiments of the present disclosure;and

FIG. 4B illustrates an exemplary process for predicting policytransaction behavior for use with embodiments of the present disclosure.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

Various embodiments of the present disclosure now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the disclosure are shown. Indeed, thedisclosure may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” is used herein in both the alternativeand conjunctive sense, unless otherwise indicated. The terms“illustrative” and “exemplary” are used to be examples with noindication of quality level. Like numbers refer to like elementsthroughout.

Brief Overview

Various embodiments of the disclosure generally relate to a predictionsystem that supports providing policies for external plots. Each of theexternal plots can have one or more structures thereon, and one or morehazards associated therewith.

The methods, apparatus and computer program products described hereinare operable to predict a likelihood that a policy transaction (e.g.,termination, loss) will occur in a future network time period. Theprediction is based on a machine learning model, which may use thefollowing: learned relationships within digital image files representingan external plot (between individual pixels and groups of pixels), plothazard score (determined by a count and severity of identified hazards),and an underwriting decision. The prediction may also be used todetermine whether additional inspection of a plot is necessary.

The methods, apparatus and computer program products described hereinare further operable to predict a likelihood that a hazardous label willoccur in a digital image file associated with a plot in a future networktime period. The prediction is based on a machine learning model usinglearned relationships within digital image files representing anexternal plot (between individual pixels and groups of pixels). Theprediction may also be used to determine whether properties of a plotmeet eligibility criteria without the need for an in-person inspection.

Visually extracting image-based plot properties from digital image filesis tedious and time-intensive. Also, making individual assessments ofplots based upon the plot's properties can be fraught with mistakes andbe data intensive. The inventors have identified that the systemresources and time allocated to such evaluations are easily exhaustedand compromised as a result of these challenges. The inventors havedetermined that the ability to programmatically normalize digital imagefiles, extract plot properties from them, and predict policytransactions would reduce the computing resources and time necessary toperform plot evaluations. The inventors have further determined thataccuracy of predicted policy transactions is improved as a result of thepresently disclosed embodiments, resulting in a reduction of computingresources and time necessary to support unnecessarily granted policies.

Exemplary Definitions

As used herein, the terms “data,” “content,” “information,” and similarterms may be used interchangeably to refer to data capable of beingtransmitted, received, and/or stored in accordance with embodiments ofthe present disclosure. Thus, use of any such terms should not be takento limit the spirit and scope of embodiments of the present disclosure.Further, where a computing device is described herein to receive datafrom another computing device, it will be appreciated that the data maybe received directly from another computing device or may be receivedindirectly via one or more intermediary computing devices, such as, forexample, one or more servers, relays, routers, network access points,base stations, hosts, and/or the like, sometimes referred to herein as a“network.” Similarly, where a computing device is described herein tosend data to another computing device, it will be appreciated that thedata may be sent directly to another computing device or may be sentindirectly via one or more intermediary computing devices, such as, forexample, one or more servers, relays, routers, network access points,base stations, hosts, and/or the like.

The term “client device” or “computing device” refers to computerhardware and/or software that is configured to access a service madeavailable by a server. The server is often (but not always) on anothercomputer system, in which case the client device accesses the service byway of a network. Client devices may include, without limitation, smartphones, tablet computers, laptop computers, wearables, personalcomputers, enterprise computers, and the like.

The terms “external plot,” “plot,” or “external plot of physical land”refer to a plot of land or any structure that is under review forinsurance policy eligibility, risk, and pricing. For example, anexternal plot may be a farm with a house, a barn, a swimming pool, andone or more trees. In embodiments, an external plot of physical land isassociated with an external plot of physical land identifier. Inembodiments, the external plot of physical land identifier is one ormore items of data by which an external plot of physical land may beidentified. For example, an external plot of physical land identifiermay comprise ASCII text, a pointer, a memory address, and the like. Inembodiments, each external plot of land may have a feature vector orother data structure containing feature data, metadata, or policyproperty data associated therewith.

The term “structure” refers to any constructed structure present on anexternal plot of physical land. In embodiments, a structure isassociated with a structure identifier. In embodiments, a structureidentifier is one or more items of data by which a structure may beidentified. For example, a structure identifier may comprise ASCII text,a pointer, a memory address, and the like.

The term “digital image file” refers to a file containing image data.Example of digital image files include .jpg, .jpeg, .png, and the like.In embodiments, a digital image file is associated with a digital imagefile identifier. In embodiments, a digital image file identifier is oneor more items of data by which a digital image file may be identified.For example, digital image file identifier may comprise ASCII text, apointer, a memory address, and the like. In embodiments, each digitalimage file may have a feature vector or other data structure containingfeature data or other metadata associated therewith.

The term “field of view” refers to an extent of observability of anexternal plot of physical land provided by a digital image file. Digitalimage files herein may be aerial, satellite, street, and externalphotos, having associated fields of view. In embodiments, a field ofview may be associated with a field of view type or identifier.

The term “policy” refers to a contractual agreement for liability orinsurance coverage for an external plot of physical land and/orstructures thereon.

The term “policy identifier” refers to one or more items of data bywhich a policy may be identified. For example, a policy identifier maycomprise ASCII text, a pointer, a memory address, and the like.

The term “policy data structure” refers to a data structure comprisingdata records associated with a policy and an external plot of physicalland. In embodiments, a policy data structure may comprise a recordcontaining a policy identifier and an external plot of physical landidentifier.

The term “policy transaction” refers to any transaction occurringrelated to a policy. Examples of transaction can be a policytermination, a policy lapse, a policy creation, and the like. Inembodiments, a policy transaction may be associated with a policytransaction identifier. In embodiments, a policy transaction identifieris one or more items of data by which a policy transaction may beidentified. For example, policy transaction identifier may compriseASCII text, a pointer, a memory address, and the like.

The term “policy transaction behavior” refers to interaction between aclient and a policy, whereby the client influences the policytransaction through their electronic behavior.

The term “policy transaction prediction” refers to a programmaticallygenerated likelihood that a policy transaction will occur based on thepresence of a particular plot property associated with an external plotof physical land. In embodiments, the policy transaction prediction isgenerated using a trained machine learning model.

The terms “plot properties” or “plot property” refer to any property orproperties associated with an external plot. Examples of plot propertiesinclude previous policy cancelations, known hazards on the plot, and thelike. Examples are further expanded on below. In embodiments, a plotproperty is associated with a plot property identifier. In embodiments,a plot property identifier is one or more items of data by which a plotproperty may be identified. For example, a plot property identifier maycomprise ASCII text, a pointer, a memory address, and the like.

The terms “image-based plot properties” or “image-based plot property”refer to any property or properties associated with an external plotthat are derived through extracting information from digital imagefiles. Examples of image-based plot properties include a tree overhangon a roof, presence of solar panels on a roof, presence of skylightwindows on a roof, presence of unnatural objects on a roof top, roofmaterial, roof surface, roof pitch, detached structures on the externalplot of physical land, presence of a pool on the external plot ofphysical land, a pool shape, presence of barriers around insuredproperty and between adjacent properties, distance to brush and trees,buffer between house or structure and surrounding vegetation, presenceof ADT, presence of a “Beware Of Dog” sign, presence of a “Do NotTrespass” sign, cracks in a driveway, peeling paint from a structure, orsigns of rust on a dwelling and the like. Examples are further expandedbelow. In embodiments, an image-based plot property is associated withan image-based plot property identifier. In embodiments, an image-basedplot property identifier is one or more items of data by which animage-based plot property may be identified. For example, an image-basedplot property identifier may comprise ASCII text, a pointer, a memoryaddress, and the like.

The term “predicted image-based plot property” refers to a predictedclassification assigned to a group of pixels contained within one ormore digital image files. In embodiments, the predicted classificationis generated using a trained machine learning model and extracted pixelsfrom a plurality of digital image files associated with an external plotof physical land. In embodiments, a predicted image-based plot propertyhas associated therewith a confidence measure representative of alikelihood that the predicted image-based plot property is anactual/correct image-based plot property present in the one or moredigital image files.

The term “currency value” refers to a digital amount of currencyassigned to a policy. The currency value may be related to a replacementcost associated with the external plot of physical land and structuresthereon or associated therewith.

The term “risk value” refers to a digital level of risk assigned to apolicy. For example, a policy might be considered “high risk” if thereis a prediction that a policy transaction (e.g., termination) will occurin the near future or if there are several known hazards on theproperty.

The term “hazard score” refers to a score assigned to an external plotbased on a number of identified hazards on the external plot and aseverity measure associated with each hazard.

The term “underwriting status” refers to whether there is a policycurrently in force associated with a particular external plot. Anunderwriting status may be one of in force or canceled.

The term “aggregated policy transaction prediction” refers to aprogrammatically generated likelihood that a policy transaction willoccur subsequent an insurance policy issuance for an external plot ofphysical land. In embodiments, an aggregated policy transactionprediction is generated based upon a combination of policy transactionpredictions associated with plot properties associated with the externalplot of physical land. In embodiments, different plot properties areassigned different weights in order to generate the aggregate policytransaction prediction.

Machine learning is a subfield of computer science that gives computersthe ability to learn without being explicitly programmed. Evolved fromthe study of pattern recognition and computational learning theory inartificial intelligence, machine learning explores the study andconstruction of algorithms that can learn from and make predictions ondata. Such algorithms overcome following strictly static programinstructions by making data-driven predictions or decisions throughbuilding a machine learning model from sample inputs. Machine learningis employed in a range of computing tasks where designing andprogramming explicit algorithms with good performance is difficult orinfeasible.

Machine learning enables prediction-making through the use of computers.Machine learning can be unsupervised for exploratory data analysis.Machine learning can also be unsupervised and be used to learn andestablish baseline behavioral profiles for various entities and thenused to find meaningful anomalies.

Machine learning is used to devise complex models and algorithms thatlend themselves to prediction; also known as predictive analytics. Thesemachine learning models allow researchers, data scientists, engineers,and analysts to produce reliable, repeatable decisions and results, anduncover hidden insights through learning from historical relationshipsand trends in the data.

The term “machine learning model” refers to a machine learning task. Amachine learning model typically comprises a title and encompasses oneor more target variables.

The term “target variable” refers to a value that a machine learningmodel is designed to predict. In the present embodiments, historicaldata is used to train a machine learning model to predict the targetvariable. Historical observations of the target variable are used forsuch training.

The term “machine learning model identifier” refers to one or more itemsof data by which a machine learning model may be identified. Forexample, a machine learning model identifier may comprise ASCII text, apointer, a memory address, and the like. In embodiments, a first machinelearning model may be associated with a first machine learning modelidentifier and a second machine learning model may be associated with asecond machine learning model identifier, and the like.

It should be appreciated that the terms “programmatically expected,”“infer,” “inferred,” or “inference” indicate machine prediction ofoccurrence of certain events. For example, a “programmatically expected”or “programmatically generated” likelihood is a value or numberdetermined by machine prediction specifying whether a policy transactionwill occur in a future network period.

The term “likelihood” refers to a measure of probability for occurrenceof a particular event. For example, the likelihood that a policytransaction will occur may be a value associated with a specific scale.In some implementations, the machine predictions discussed above arebased, at least in part, on the “likelihood” that an event will occur.

Example System Architecture

Methods, apparatuses, and computer program products of the presentdisclosure may be embodied by any of a variety of devices. For example,the method, apparatus, and computer program product of an exampleembodiment may be embodied by a networked device (e.g., an enterpriseplatform), such as a server or other network entity, configured tocommunicate with one or more devices, such as one or more clientdevices. Additionally or alternatively, the computing device may includefixed computing devices, such as a personal computer or a computerworkstation. Still further, example embodiments may be embodied by anyof a variety of mobile devices, such as a portable digital assistant(PDA), mobile telephone, smartphone, laptop computer, tablet computer,wearable, or any combination of the aforementioned devices.

FIG. 1 illustrates an example computing system within which embodimentsof the present disclosure may operate. Client devices may access apolicy transaction prediction system 115 via a communications network102 (e.g., the Internet, LAN, WAN, or the like) using client devices101A-101N. The policy transaction prediction system 115 may comprise aserver 107 in communication with one or more databases or repositories108, 109, 110.

The server 107 may be embodied as a computer or computers as known inthe art. The server 107 may provide for receiving of electronic datafrom various sources, including but not necessarily limited to theclient devices 101A-101N. For example, the server 107 may be operable toreceive and process search requests provided by the client devices101A-101N. The server 107 may facilitate the generation and providing ofvarious search results comprising digital content items. The server 107may include a prediction module 103 for predicting transaction behaviorand an image processing module 104 for processing received digital imagefiles.

The databases or repositories 108-110 may be embodied as a data storagedevice such as a Network Attached Storage (NAS) device or devices, or asa separate database server or servers. The databases 108-110 includeinformation accessed and stored by the server 107 to facilitate theoperations of the policy transaction prediction system 115. For example,the databases 108-110 may include, without limitation, plot information,plot properties, and image based plot properties, and/or the like.

Plot repository 109 may include a plot profile for each plot of thepolicy transaction prediction system 115. The plot profile includesidentifying information about a plot of land, including a geographiclocation in the form of GPS coordinates and other attributes. A plotprofile may be created upon registration with the policy transactionprediction system 115.

Plot property repository 110 may include a plurality of data structuresrepresenting properties associated with a plot. Properties associatedwith a plot can include examples listed below.

Image-based plot property repository 108 may include digital image filesuploaded to the policy transaction prediction system 115 from clientdevices 101A-101N as well as properties extracted and coded from thedigital image files as described herein.

The client devices 101A-101N may be any computing device as definedabove. Electronic data received by the server 107 from the clientdevices 101A-101N may be provided in various forms and via variousmethods. For example, the client devices 101A-101N may include desktopcomputers, laptop computers, smartphones, netbooks, tablet computers,wearables, and the like.

In embodiments where a client device 101A-101N is a mobile device, suchas a smart phone or tablet, the client device 101A-101N may execute oneor more “apps” to interact with the policy transaction prediction system115. Such apps are typically designed to execute on mobile devices, suchas tablets or smartphones. For example, an app may be provided thatexecutes on mobile device operating systems such as iOS®, Android®, orWindows®. These platforms provide frameworks that allow apps tocommunicate with one another and with particular hardware and softwarecomponents of mobile devices. For example, the mobile operating systemsnamed above each provide frameworks for interacting with locationservices circuitry, wired and wireless network interfaces, usercontacts, and other applications. Communication with hardware andsoftware modules executing outside of the app is typically provided viaapplication programming interfaces (APIs) provided by the mobile deviceoperating system.

Additionally or alternatively, the client device 101A-101N may interactwith the policy transaction prediction system 115 via a web browser. Asyet another example, the client device 101A-101N may include varioushardware or firmware designed to interface with the policy transactionprediction system 115.

Via an interaction method described above, the client device 101A-101Nmay upload digital image files to the policy transaction predictionsystem 115.

An example of a data flow for exchanging electronic information amongone or more client devices 101A-101N and the policy transactionprediction system 115 is described below.

In the depicted embodiment, the one or more client devices 101A-101N areconfigured to transmit digital image files and other data to the policytransaction prediction system 115. The digital image files and otherdata are transmitted by wired or wireless communication over thecommunications network 102 to the policy transaction prediction system115. The one or more client devices 101A-101N may receive communicationover the communications network 102 from the policy transactionprediction system, and the communication may be transmitted by wired orwireless communication.

Example Apparatus for Implementing Embodiments of the Present Disclosure

The server 107 may be embodied by one or more computing systems, such asapparatus 200 shown in FIG. 2. The apparatus 200 may include a processor202, a memory 201, input/output circuitry 204, communications circuitry205, image processing circuitry 203, and prediction circuitry 206. Theapparatus 200 may be configured to execute the operations describedherein. Although these components 201-206 are described with respect tofunctional limitations, it should be understood that the particularimplementations necessarily include the use of particular hardware. Itshould also be understood that certain of these components 201-206 mayinclude similar or common hardware. For example, two sets of circuitrymay both leverage use of the same processor, network interface, storagemedium, or the like to perform their associated functions, such thatduplicate hardware is not required for each set of circuitry.

The use of the term “circuitry” as used herein with respect tocomponents of the apparatus should therefore be understood to includeparticular hardware configured to perform the functions associated withthe particular circuitry as described herein. That is, the term“circuitry” should be understood broadly to include hardware and, insome embodiments, software for configuring the hardware. For example, insome embodiments, “circuitry” may include processing circuitry, storagemedia, network interfaces, input/output devices, and the like. In someembodiments, other elements of the apparatus 200 may provide orsupplement the functionality of particular circuitry. For example, theprocessor 202 may provide processing functionality, the memory 201 mayprovide storage functionality, the communications circuitry 205 mayprovide network interface functionality, and the like.

In some embodiments, the processor 202 (and/or co-processor or any otherprocessing circuitry assisting or otherwise associated with theprocessor) may be in communication with the memory 201 via a bus forpassing information among components of the apparatus. The memory 201may be non-transitory and may include, for example, one or more volatileand/or non-volatile memories. In other words, for example, the memorymay be an electronic storage device (e.g., a computer readable storagemedium). The memory 201 may be configured to store information, data,content, applications, instructions, or the like, for enabling theapparatus to carry out various functions in accordance with exampleembodiments of the present disclosure.

The processor 202 may be embodied in a number of different ways and may,for example, include one or more processing devices configured toperform independently. Additionally or alternatively, the processor mayinclude one or more processors configured in tandem via a bus to enableindependent execution of instructions, pipelining, and/ormultithreading. The use of the term “processing circuitry” may beunderstood to include a single core processor, a multi-core processor,multiple processors internal to the apparatus, and/or remote or “cloud”processors.

In an example embodiment, the processor 202 may be configured to executeinstructions stored in the memory 201 or otherwise accessible to theprocessor. Alternatively, or additionally, the processor may beconfigured to execute hard-coded functionality. As such, whetherconfigured by hardware or software methods, or by a combination thereof,the processor may represent an entity (e.g., physically embodied incircuitry) capable of performing operations according to an embodimentof the present disclosure while configured accordingly. Alternatively,as another example, when the processor is embodied as an executor ofsoftware instructions, the instructions may specifically configure theprocessor to perform the algorithms and/or operations described hereinwhen the instructions are executed.

In some embodiments, the apparatus 200 may include input/outputcircuitry 204 that may, in turn, be in communication with processor 202to provide output to the user and, in some embodiments, to receive anindication of a user input. The input/output circuitry 204 may comprisea user interface and may include a display and may comprise a web userinterface, a mobile application, a client device, a kiosk, or the like.In some embodiments, the input/output circuitry 204 may also include akeyboard, a mouse, a joystick, a touch screen, touch areas, soft keys, amicrophone, a speaker, or other input/output mechanisms. The processorand/or user interface circuitry comprising the processor may beconfigured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,memory 204, and/or the like).

The communications circuitry 205 may be any means such as a device orcircuitry embodied in either hardware or a combination of hardware andsoftware that is configured to receive and/or transmit data from/to anetwork and/or any other device, circuitry, or module in communicationwith the apparatus 200. In this regard, the communications circuitry 205may include, for example, a network interface for enablingcommunications with a wired or wireless communication network. Forexample, the communications circuitry 205 may include one or morenetwork interface cards, antennae, buses, switches, routers, modems, andsupporting hardware and/or software, or any other device suitable forenabling communications via a network. Additionally or alternatively,the communication interface may include the circuitry for interactingwith the antenna(s) to cause transmission of signals via the antenna(s)or to handle receipt of signals received via the antenna(s).

Image processing circuitry 203 includes hardware configured to receiveand process digital image files. The image processing circuitry 203 mayutilize processing circuitry, such as the processor 202, to performthese actions. The image processing circuitry 203 may send and/orreceive data from prediction circuitry 206. In some implementations, thesent and/or received data may be data identifying image-based plotproperties associated with one or more external plots. It should also beappreciated that, in some embodiments, the image processing circuitry203 may include a separate processor, specially configured fieldprogrammable gate array (FPGA), or application specific interfacecircuit (ASIC). In some implementations, prediction circuitry 206,described below, may be sub-circuitry belonging to image processingcircuitry 203. The image processing circuitry 203 may be implementedusing hardware components of the apparatus configured by either hardwareor software for implementing the functions described herein.

Prediction circuitry 206 includes hardware configured to predict that apolicy transaction will occur in a future network period. In someimplementations, the prediction is specified as a probability orlikelihood that the policy transaction will be a termination in thefuture network period and/or identify hazards or variables in digitalimage files. The prediction circuitry 206 may utilize processingcircuitry, such as the processor 202, to perform these actions. However,it should also be appreciated that, in some embodiments, the predictioncircuitry may include a separate processor, specially configured FPGA,or ASIC for performing the functions described herein. Circuitry 206 maybe implemented using hardware components of the apparatus configured byeither hardware or software for implementing these planned functions.

As will be appreciated, any such computer program instructions and/orother type of code may be loaded onto a computer, processor or otherprogrammable apparatus's circuitry to produce a machine, such that thecomputer, processor or other programmable circuitry that execute thecode on the machine create the means for implementing various functions,including those described herein.

It is also noted that all or some of the information presented by theexample displays discussed herein can be based on data that is received,generated and/or maintained by one or more components of apparatus 200.In some embodiments, one or more external systems (such as a remotecloud computing and/or data storage system) may also be leveraged toprovide at least some of the functionality discussed herein.

As described above and as will be appreciated based on this disclosure,embodiments of the present disclosure may be configured as methods,mobile devices, backend network devices, and the like. Accordingly,embodiments may comprise various means including entirely of hardware orany combination of software and hardware. Furthermore, embodiments maytake the form of a computer program product on at least onenon-transitory computer-readable storage medium having computer-readableprogram instructions (e.g., computer software) embodied in the storagemedium. Any suitable computer-readable storage medium may be utilizedincluding non-transitory hard disks, CD-ROMs, flash memory, opticalstorage devices, or magnetic storage devices.

Example Processes for Policy Transaction Behavior Prediction

As described, a policy providing service may utilize available data toprovide policy requesters with available policies related to externalplots, including risk assessment and pricing related to the policy andexternal plot.

In order to accurately gauge risk associated with providing a policyrelated to a particular external plot, a policy provider must gatherseveral types of data about the external plot. This aids in programmaticdecision-making as to whether to provide the policy and what to chargefor the policy. As an example, a policy associated with a particularplot may be considered high risk and therefore the policy provider willopt not to grant a policy for the plot. Or, the policy provider mayprovide the policy at a greater cost than if the policy were to beconsidered low risk.

Naturally, the ability to accurately predict policy transaction behaviorimpacts resource availability and efficient use thereof. Accordingly,there exists a dire market need for methods and systems that canaccurately predict policy transactions (e.g., terminations) that willoccur associated with a particular external plot.

In the present embodiments, a plurality of machine learning models areemployed to provide aggregated policy transaction predictions. Themachine learning models are trained using various types of data.

For example, digital image files are collected through home inspections(through the use of digital image capturing means such as a computingdevice having a digital camera installed thereon) as well as receivedelectronically over a network. For example, a certain number of homesthat bind an insurance policy will be inspected by a third party vendoreach year. This inspection consists of digital imagery collected of theproperty as well as a checklist of hazards to identify. Theseinspections result in the capture of a set of images of a property, aswell as a list of hazards found at a property.

Further, publicly licensed digital images of common outdoor items areavailable for use. For example, trees, windows, roofs, and the like, arerepresented in a large plurality of digital image files. These publiclylicensed digital image files supplement the training of the model thatis done using the inspection-related images so that some data isguaranteed to be clean and separated from the above training data.

In embodiments, classes of data are identified/used for training themachine learning model. Examples of classes that are identified includeoverhanging branches, missing and/or rusted gutters, broken windows,cracked foundations, broken stairs, cracked or broken chimney, crackedor peeling exterior paint, cracks or holes in the soffits, presence of apool, presence of a fence, “ADT” signs, roof characteristics, andfinally “poor maintenance”. As can be imagined, some of these categoriesare broad and/or not specific. These are also subject to inspector bias,therefore the present embodiments are flexible accordingly.

In embodiments, a deep learning convolutional neural network serves as atop layer of machine learning for the present disclosure. Inembodiments, a tree based machine learning model is used for the bottomand prediction layer.

In embodiments, the deep learning model is a convolutional neuralnetwork for image recognition style problems. In embodiments, a deeplearning model has 50 layers of learning. In embodiments, the deeplearning model examines a digital image on a pixel level, and look atboth individual pixels and relation of groups of pixels to another. Inembodiments, the deep learning model provides a set of predictions ofwhat is in the image and where everything is in relation to one another.In embodiments, the deep learning model systematically moves a slidingwindow over the image, providing predictions for all items in the windowprovided.

In embodiments, given an arbitrary number of images, the deep learningmodel model will describe all of them. This fact is important, as thenumber of images associated with a given inspection is not known priorto being fed into the model. The model will append all of thepredictions into a string of numbers, which are fed into the next (i.e.,tree based) model.

In embodiments, the tree based machine learning model will, given adescription of a set of digital images (e.g., from the first deeplearning machine learning model), provide a prediction of whether ahazard class exists in any of the images.

In embodiments, the tree based machine learning model evaluates thedescription of the set of images on an aggregate basis. In embodiments,the tree based machine learning model reviews the predicted locations ofobjects/properties in relation to one another across an entire set ofimages associated with an external plot of physical land. In so doing,the machine learning model is provided with context to work around anydepth perception issues, as well as provide robustness to arbitrarynumbers of images.

In embodiments, the tree based machine learning model also receivesadditional data associated with the digital images, such as policyrelated information. For example, digital images associated with anexternal plot of land are received from a policy holder. As such, thosedigital images can be associated with any policy or policy relatedinformation associated with the policy holder. The policy relatedinformation can be used for predicting policy transaction behavior.

In embodiments, policy related and/or external plot of land information(e.g., features of feature vectors associated therewith) can include oneor more of coverage per square foot, days between policy purchase andeffective date, number of adults on the policy, age of the insured onthe policy, count of prior losses, and existence of prior insurance.

FIG. 3 is a flow chart of an example process 300 for plot risk selectionand pricing or other determination for use with embodiments of thepresent disclosure. In some embodiments, images are collected andprocessed before being input into a machine learning model. Relevantfeatures associated with an external plot and potential new policy areextracted from the model and examined against a prior loss experiencemodel. If a level of risk produced by the prior loss experience model isnot acceptable, a quote for the potential policy is stopped. If thelevel of risk produced by the prior loss experience model is determinedto be acceptable, the relevant features are complemented with geospatialinformation so that a determination about the policy can be made, forexample a price for the policy can be provided.

FIGS. 4A-4B illustrate an exemplary process 400 for predicting policytransaction behavior for use with embodiments of the present disclosure.The process 400 begins with receiving 401 from a remote computingdevice, a plurality of digital image files associated with an externalplot of physical land. In embodiments, each digital image file of theplurality of digital image files is representative of a different fieldof view of the external plot of physical land.

In embodiments, the process 400 continues with extracting 402, from eachdigital image file of the plurality of digital image files, one or moreimage-based plot properties associated with the external plot ofphysical land. In embodiments, the extracting is performed on a pixel bypixel basis and using a first trained machine learning model. Inembodiments, the first trained machine learning model outputs apredicted image-based plot property based on one or more pixels of adigital image file and a confidence measure associated with thepredicted image-based plot property.

In embodiments, the process 400 continues with generating 403, for eachimage-based plot property and using a second trained machine learningmodel, a policy transaction prediction, the policy transactionprediction being a programmatically generated likelihood that theimage-based plot property will lead to a policy transaction occurrence.

In embodiments, a hazard score for the external plot of physical land isgenerated by the second machine learning model. In embodiments, thepolicy transaction prediction is based at least on the hazard score.

In embodiments, the hazard score is generated based at least on a countof the hazardous image-based plot properties and a severity of each ofthe one or more hazardous image-based plot properties.

In embodiments, the process 400 continues with, based on the policytransaction predictions, generating 404 an aggregated policy transactionprediction for the external plot of physical land, the aggregated policytransaction prediction being a programmatically generated likelihoodthat a policy transaction will occur subsequent an insurance policyissuance for the external plot of physical land. In embodiments, theaggregated policy transaction prediction is based on a plurality ofpolicy transaction predictions associated with the external plot ofphysical land.

In embodiments, the process comprises generating the aggregated policytransaction prediction for the external plot of physical land prior toan issuance of an insurance policy for the external plot of physicalland.

In some embodiments, if a policy provider accepts a level of risk ofpredicted policy transaction behavior, the provider may complementfeatures with geospatial information to provide pricing for a policy.

In embodiments, the policy transaction is a policy termination.

In embodiments, the policy termination is initiated by an entity issuingthe insurance policy for the external plot of physical land.

In embodiments, the external plot of physical land includes one or morestructures.

In embodiments, each digital image file of the plurality of digitalimage files is normalized.

In embodiments, the process comprises assigning a price value to aninsurance policy for the external plot of physical land based at leaston the aggregated policy transaction prediction.

In embodiments, the process comprises assigning a risk value to aninsurance policy for the external plot of physical land based at leaston the aggregated policy transaction prediction.

In embodiments, an image-based plot property of the plurality of plotproperties is one of a tree overhang on a roof, presence of solar panelson a roof, presence of skylight windows on a roof, presence of unnaturalobjects on a roof top, roof material, roof surface, roof pitch, detachedstructures on the external plot of physical land, presence of a pool onthe external plot of physical land, a pool shape, presence of barriersaround insured property and between adjacent properties, distance tobrush and trees, buffer between house or structure and surroundingvegetation, presence of ADT, presence of a “Beware Of Dog” sign,presence of a “Do Not Trespass” sign, cracks in a driveway, peelingpaint from a structure, or signs of rust on a dwelling.

In embodiments, the process comprises determining that one or more ofthe image-based plot properties represents one or more hazardousimage-based plot properties, assigning a severity to each hazardousimage-based plot property of the one or more hazardous image-based plotproperties, and returning an indication of each of the one or morehazardous image-based plot properties.

In embodiments, the process comprises receiving an underwriting statusand input the underwriting status into the second machine learningmodel.

In embodiments, the first machine learning model is a convolutionalneural network.

In embodiments, the first machine learning model is trained using imagesof known plot properties.

In embodiments, the first machine learning model provides image-basedplot properties based upon any arbitrary number of received digitalimage files.

In embodiments, the second machine learning model is a tree basedmachine learning model.

In embodiments, the second machine learning model is trained using knownplot properties and known image-based plot properties.

In embodiments, the second machine learning model predicts whether ahazard exists in any of the image-based plot properties.

In embodiments, the second machine learning model uses the existence ofa hazard to provide a policy transaction prediction.

Example Image-Based Plot Properties and Prediction Processes

It should be understood that each of the plot properties and image-basedplot properties discussed in this application, may be in someembodiments, be primary or secondary properties. A list of exemplaryproperties is provided below.

Table A shows example plot properties and image-based plot properties.

TABLE A Property Features Roof Tree overhang on roof, presence of solarpanels on roof, Related presence of skylight windows on roof, presenceof unnatural objects on roof top, roof material, shape/facets of roof,slope/pitch of roof, use of multiple material types/colors on roof PlotDetached structures on plot (count/type), presence of pool, Relatedshape of pool, condition of pool, barriers to pool, presence of barriersaround insured property and between adjacent properties (e.g., fences),distance to brush and trees, info on buffer between house andsurrounding vegetation, and/or the like. Customer Presence of securitysystem, Beware of Dog, Do Not Related Trespass signs, cracks indriveway, peeling paint, signs of rust on dwelling, upkeep, and/or thelike.

Examples of Properties that May be Associated with a Plot or StructureAlso Include

Information/data regarding the building materials used to build thestructure, finishings (e.g., toilets, vanities, cabinets, wallpaper,paint, flooring, lighting fixtures, etc.) used to finish one or morespaces within the structure, furnishings (e.g., couches, tables, chairs,desks, beds, bookshelves, window dressings, etc.) used to furnish thestructure, appliances and structure systems (e.g., HVAC system, hotwater heater, security system, refrigerator, stove, microwave,dishwasher, clothes washer and dryer) housed within and/or servicing thestructure, electronic devices (e.g., televisions, computers, smartbuilding devices, gaming systems, etc.), personal effects (e.g.,clothes, jewelry, toiletries, purses, shoes, linens, etc.) and otheritems used to build, finish, or furnish the structure and/or housedwithin the structure may be stored associated with the plot. Forexample, the information/data regarding incident information/datacorresponding to one or more incidents involving the structure or plot(e.g., tree limb falling on roof, slip and fall on the front steps,and/or the like); and/or other information/data corresponding to theproperty may be stored associated with the plot.

In some implementations, hazardous image-based plot properties caninclude tree branch overhang on roof, the presence of a pool,missing/unstable/broken steps, and the presence or lack of fences.

In some implementations, one property may represent category data,specifically residential plots. To determine a normalized value of theone property, an algorithm may be executed on associated raw datarelated to an external plot. For example, if the category dataassociated with an external plot is “residential,” the value of theproperty is 1. If the category data associated with the external plot isnot “residential,” the value of the property is 0.

In some embodiments, a number of example properties relate to categorydata and sub-category data. There is also a number of properties relatedto location data.

Similar properties may be clustered, grouped, or aggregated. Theclustered or the non-clustered properties may be used to train a machinelearning model. It should be understood that the selection of propertiesor clusters of attributes for training machine learning models or forpolicy transaction prediction processes can greatly affect therespective performance. In some implementations, properties and/orclusters of properties are selected based on statistical analysis.

Details regarding various embodiments are described in the attachedappendices, the contents of which are hereby herein incorporated byreference in their entirety.

Additional Implementation Details

Although an example processing system has been described in FIG. 2,implementations of the subject matter and the functional operationsdescribed herein can be implemented in other types of digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them.

Embodiments of the subject matter and the operations described hereincan be implemented in digital electronic circuitry, or in computersoftware, firmware, or hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. Embodiments of the subject matter describedherein can be implemented as one or more computer programs, e.g., one ormore modules of computer program instructions, encoded on computerstorage medium for execution by, or to control the operation of,information/data processing apparatus. Alternatively, or in addition,the program instructions can be encoded on an artificially-generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, which is generated to encode information/datafor transmission to suitable receiver apparatus for execution by aninformation/data processing apparatus. A computer storage medium can be,or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described herein can be implemented as operationsperformed by an information/data processing apparatus oninformation/data stored on one or more computer-readable storage devicesor received from other sources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus can includespecial purpose logic circuitry, e.g., an FPGA or an ASIC. The apparatuscan also include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor information/data (e.g., one or more scripts stored in a markuplanguage document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub-programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described herein can be performed by oneor more programmable processors executing one or more computer programsto perform actions by operating on input information/data and generatingoutput. Processors suitable for the execution of a computer programinclude, by way of example, both general and special purposemicroprocessors, and any one or more processors of any kind of digitalcomputer. Generally, a processor will receive instructions andinformation/data from a read-only memory or a random access memory orboth. The essential elements of a computer are a processor forperforming actions in accordance with instructions and one or morememory devices for storing instructions and data. Generally, a computerwill also include, or be operatively coupled to receive information/datafrom or transfer information/data to, or both, one or more mass storagedevices for storing data, e.g., magnetic, magneto-optical disks, oroptical disks. However, a computer need not have such devices. Devicessuitable for storing computer program instructions and information/datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described herein can be implemented on a computer having adisplay device, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information/data to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Embodiments of the subject matter described herein can be implemented ina computing system that includes a back-end component, e.g., as aninformation/data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a web browserthrough which a user can interact with an implementation of the subjectmatter described herein, or any combination of one or more suchback-end, middleware, or front-end components. The components of thesystem can be interconnected by any form or medium of digitalinformation/data communication, e.g., a communication network. Examplesof communication networks include a local area network (“LAN”) and awide area network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits information/data (e.g., an HTML page) toa client device (e.g., for purposes of displaying information/data toand receiving user input from a user interacting with the clientdevice). Information/data generated at the client device (e.g., a resultof the user interaction) can be received from the client device at theserver.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described herein in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

CONCLUSION

Many modifications and other embodiments of the disclosures set forthherein will come to mind to one skilled in the art to which thesedisclosures pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the disclosures are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

What is claimed is:
 1. An apparatus for predicting policy transactionbehavior, the apparatus comprising at least one processor and at leastone memory including computer program code, the at least one memory andthe computer program code configured to, with the at least oneprocessor, cause the apparatus to: receive, from a remote computingdevice, a plurality of digital image files associated with the externalplot of physical land, each digital image file of the plurality ofdigital image files representative of a different field of view of theexternal plot of physical land; extract, from each digital image file ofthe plurality of digital image files, one or more image-based plotproperties associated with the external plot of physical land, whereinthe extracting is performed on a pixel by pixel basis and using a firsttrained machine learning model, and wherein the first trained machinelearning model outputs a predicted image-based plot property based onone or more pixels of a digital image file and a confidence measureassociated with the predicted image-based plot property; generate, foreach image-based plot property and using a second trained machinelearning model, a policy transaction prediction, the policy transactionprediction being a programmatically generated likelihood that theimage-based plot property will lead to a policy transaction occurrence;based on the policy transaction predictions, generate an aggregatedpolicy transaction prediction for the external plot of physical land,the aggregated policy transaction prediction being a programmaticallygenerated likelihood that a policy transaction will occur subsequent aninsurance policy issuance for the external plot of physical land; andtransmit, to a requesting computing device, the aggregated policytransaction prediction.
 2. The apparatus of claim 1, further configuredto generate the aggregated policy transaction prediction for theexternal plot of physical land prior to an issuance of an insurancepolicy for the external plot of physical land.
 3. The apparatus of claim2, wherein the policy transaction is a policy termination.
 4. Theapparatus of claim 3, wherein the policy termination is initiated by anentity issuing the insurance policy for the external plot of physicalland.
 5. The apparatus of claim 1, wherein the external plot of physicalland includes one or more structures.
 6. The apparatus of claim 1,wherein the apparatus is further caused to assign a price value to aninsurance policy for the external plot of physical land based at leaston the aggregated policy transaction prediction.
 7. The apparatus ofclaim 1, wherein the apparatus is further caused to assign a risk valueto an insurance policy for the external plot of physical land based atleast on the aggregated policy transaction prediction.
 8. The apparatusof claim 1, wherein an image-based plot property of the plurality ofplot properties is one of a tree overhang on a roof, presence of solarpanels on a roof, presence of skylight windows on a roof, presence ofunnatural objects on a roof top, roof material, roof surface, roofpitch, detached structures on the external plot of physical land,presence of a pool on the external plot of physical land, a pool shape,presence of barriers around insured property and between adjacentproperties, distance to brush and trees, buffer between house orstructure and surrounding vegetation, presence of ADT, presence of a“Beware Of Dog” sign, presence of a “Do Not Trespass” sign, cracks in adriveway, peeling paint from a structure, or signs of rust on adwelling.
 9. The apparatus of claim 1, wherein the apparatus is furthercaused to: determine that one or more of the image-based plot propertiesrepresents one or more hazardous image-based plot properties; assign aseverity to each hazardous image-based plot property of the one or morehazardous image-based plot properties; and return an indication of eachof the one or more hazardous image-based plot properties.
 10. Theapparatus of claim 9, wherein the apparatus is further caused to:generate a hazard score for the external plot of physical land; and usethe generated hazard score for generating one or more policy transactionpredictions.
 11. The apparatus of claim 10, wherein the hazard score isgenerated based at least on a count of the hazardous image-based plotproperties and a severity of each of the one or more hazardousimage-based plot properties.
 12. The apparatus of claim 1, wherein theapparatus is further caused to: receive an underwriting status and inputthe underwriting status into the second machine learning model.
 13. Theapparatus of claim 1, wherein the first machine learning model is aconvolutional neural network.
 14. The apparatus of claim 13, wherein thefirst machine learning model is trained using digital images of knownplot properties.
 15. The apparatus of claim 14, wherein the firstmachine learning model provides image-based plot properties based uponany arbitrary number of received digital image files.
 16. The apparatusof claim 1, wherein the second machine learning model is a tree basedmachine learning model.
 17. The apparatus of claim 16, wherein thesecond machine learning model is trained using known plot properties andknown image-based plot properties.
 18. The apparatus of claim 17,wherein the second machine learning model generates a programmaticallygenerated prediction representing a likelihood that a hazard exists inany of the image-based plot properties.
 19. The apparatus of claim 18,wherein the second machine learning model uses the existence or lack ofa hazard to programmatically generate a policy transaction prediction.20. A computer-implemented method for predicting policy transactionbehavior, the method comprising: receiving, using a processor and from aremote computing device, a plurality of digital image files associatedwith the external plot of physical land, each digital image file of theplurality of digital image files representative of a different field ofview of the external plot of physical land; extracting, using theprocessor, from each digital image file of the plurality of digitalimage files, one or more image-based plot properties associated with theexternal plot of physical land, wherein the extracting is performed on apixel by pixel basis and using a first trained machine learning model,and wherein the first trained machine learning model outputs a predictedimage-based plot property based on one or more pixels of a digital imagefile and a confidence measure associated with the predicted image-basedplot property; generating, using the processor, for each image-basedplot property and using a second trained machine learning model, apolicy transaction prediction, the policy transaction prediction being aprogrammatically generated likelihood that the image-based plot propertywill lead to a policy transaction occurrence; based on the policytransaction predictions, generating, using the processor, an aggregatedpolicy transaction prediction for the external plot of physical land,the aggregated policy transaction prediction being a programmaticallygenerated likelihood that a policy transaction will occur subsequent aninsurance policy issuance for the external plot of physical land; andtransmitting, using the processor and to a requesting computing device,the aggregated policy transaction prediction.